Lab 7

Above is a reference map of the Los Angeles National Station Fire of 2009. The red area indicates locations that were burnt one time or another during the fire. Also added are the hillshade regions along with the city tracts and major highways.


This was a very difficult lab due to the freedom that we were allowed. For this reason I have chosen to further explore, using GIS, the article, Where Land Slides, Trying to Learn Why, that was part of last week's reading. The article dealt with landslides and how the threat of landslides is increased after a fire has passed a region.

According to www.dictionary .com, a landslide is defined as, "The rapid downward sliding of a mass of earth and rock. Landslides usually move over a confined area. Many kinds of events can trigger a landslide, such as the oversteepening of slopes by erosion associated with rivers, glaciers, or ocean waves; heavy snowmelt which saturates soil and rock; or earthquakes that lead to the failure of weak slopes." Knowing that the slope of a region increases the possibility of landslides makes it easier to decide the manner in which to display the information on ArcGIS. We can download the topographic data from the USGS web site. Once this information is acquired, a slope map of the region that was affected can be made. As is displayed below:



This map shows the areas that have steep slopes along with a one-Mile buffer around all the burnt areas.


The article also stated that weather was a big factor in the development of landslides. "Rainfall is the key parameter for all this, so we’re always measuring rain" (Fountaine, 2). The slope's information coupled with the location of the burnt areas will help researchers share the data with other agencies, such as the "National Weather Service, which uses it and information from a network of other rain gauges to decide whether to issue local alerts about potential mudslides" (Fountaine, 2) In the thematic map that I provided, Los Angeles tracts that are at risk of landslides are highlighted in blue. Doing this makes it easier for researchers to choose appropriate locations for their equipment in order to maximize resources. This is essential when conducting first-hand research. As the article states, "We understand the process through which these landslides occur incredibly poorly" (Fountaine, 1), and acquiring useful and accurate data is essential in order to learn about landslides.

Understanding landslides and the factors that trigger them is important in order to be able to predict or anticipate the location of future landslides in order to save lives. Although the soil content of the region in question is important, all landslides have a common factor, water. The more saturated soil becomes, the more it acts as a liquid. At the point of full capacity, landslides take effect. This was the case of the La Conchita landslide in Ventura County, 1995. "The extraordinary rainfall of January 1995 probably was the principal contributing factor to the elevated ground–water levels and, hence the landslide movement." This is why it is important for agencies to be able to share information.

Using the map that I generated, local police departments can use this map in order to help them save lives. It can be seen clearly on the map, tracts that are within one mile of the burn area. One mile was used as the buffer since the La Conchita landslide traveled approximately 1,150 ft. Additionally, it can be seen that the Southern region of the burn area, has more steep hills than the North. By acknowledging this observation, emergency officials can distribute their personnel most efficiently.

References:

http://pubs.usgs.gov/of/2005/1067/508of05-1067.html

Fountaine, Henry. "Where Land Slides, Trying to Learn Why ". The New York Times. 05/25/2010 <http://www.nytimes.com/2009/10/20/science/20mud.html?_r=3>.

"landslide." The American Heritage® Science Dictionary. Houghton Mifflin Company. 25 May. 2010. <Dictionary.com http://dictionary.reference.com/browse/landslide>.

Jibson, Randall. "Landslide Hazards at La Conchita, California". USGS. 5/25/2010 <http://pubs.usgs.gov/of/2005/1067/508of05-1067.html>.

Lab 6

DEM:

Shaded Relief

Slope

Aspect

3-D

   The location that I chose for my lab was not a location that would seem to generate much appeal or project much data with regard to elevation.  This is because when I was there, it seemmed to be very flat.  Once I looked at the map on ArcGIS, though, I realized that there is much more to this location.  The location is Quantico, VA, and the significance that this location has to my life is that this is where I went to train for Officer Candidate School.  We got to familiarize ourselves with the area very well since we conducted a six, nine, and twelve mile hiken within a six-week period. 

The spatial data is as follows:
Extent:
Latitude:    From: 38.2203
                 To:     38.7258
Longitude: From: -76.9475
                 To:     -77.6222
Spatial Reference:  GCS North American 1983
Datum:                   D North American 1983

    The map that I made has many different elevation changes.  The reason that I did not notice these characteristics while I was there may have been due to foliage and vegitation densities.  Also, the map represents the area from a bird's eye view.  This view allows the user to see more data than an observer on the ground. 

Lab 5

This week we were instructed to make two world maps in three different types of projections: conformal, equal area, and equidistant. I chose to make mine on the following projections:

Conformal:
Mercator Conformal
Gall Stereographic Conformal

Equal Area:
Mollweide Equal Area
Behrmann Equal Area

Equidistant:
Conic Equidistant
Sinusoidal Equidistant

The following are the projections along with the distance of each from Washington D.C. to Kabul, Afghanistan, and around the Equator.

Mercator Conformal:
   Distance from Washington D.C. to Kabul, Afghaninstan:  10,105Mi
   Distace at the Equator:  24,957Mi
Gall Stereographic Conformal:
  Distance from Washington D.C. to Kabul, Afghaninstan: 7,168Mi
  Distace at the Equator: 17,168Mi

Mollweide Equal Area:
Distance from Washington D.C. to Kabul, Afghaninstan: 7,924Mi
Distace at the Equator: 22,437Mi
Behrmann Equal Area:
Distance from Washington D.C. to Kabul, Afghaninstan: 8,786Mi
Distace at the Equator: 21,632Mi

Conic Equidistant:
Distance from Washington D.C. to Kabul, Afghaninstan: 6,985Mi
Distace at the Equator: 25,329Mi
Sinusoidal Equidistant:
Distance from Washington D.C. to Kabul, Afghaninstan: 8,168Mi
Distace at the Equator: 24,884Mi

Map projections just by their nature will always have distortions and inaccuracies in them. What we try to do when we use maps is to try to pick a map projection that fits our purpose. For example, if we needed a bearing while on a ship at sea, we would need a map projection that retains angles well.  Conformal map projections do this well.  We can see that in conformal projections all the angles are 90 degrees, as in the maps that I posted. Unfortunately the area closest to the poles have lots of distortions that make these projections less and less useful as you move away from the standard parallel.

If our goal was to preserve area, then the equal area maps are what we would be working with. Some of these maps work well, but the angles are not preserved well as in the Mollweide Equal Area projection.  It is clear that in this map, the only 90 degree angles exist where the Prime Meridian and the Equator cross.  Although the Behrmann Equal Area Projection preserves the angles, the continents are greatly distorted and thinned out.  The only use for this map is to show the different areas.

The last projection that we worked with and the one that I think we should have been working with since the beginning is the equidistant projections. These projections focus on preserving the distance from one point to another. These projections, unfortunately, also have to be used accordingly. Since the Sinusoidal Equidistant projection is focused on the Equator, it preserves the distances from the equator. Since neither Washington D.C., nor Kabul is at the center of this map projection, the distance is not preserved. The distance that it showed from Washington D.C. to Kabul, Afghanistan was 8,168, which is 1,246 Miles off of the actual 6,922 Miles. The Conic Equidistant Projection turns out to be the closest at only 63 Miles off. This projection is closer to the actual distance because the two cities are closer to the standard parallel of the projection.

As we can see, knowing how to use map projections is just as important as being able to choose which one to use. If we would have chosen a map projection that was focused at either Washington D.C. or Kabul, our measurements would have been more accurate. Lastly, I think that some prior knowledge of the actual distance would great because without this knowledge, we could have picked the sinusoidal map projection and not have known that we were very inaccurate.

Lab 4

Although this lab was long, going through the tutorial made it a lot easier to understand ArcGIS. Since this program is new to all of us, having a set of simple instructions with visual guides made it bearable. Even with this tutorial though, I feel that there is a lot more that can be learned from this program. Like many other programs that have an unlimited amount of potential, I feel that going through this tutorial is only scratching the surface.

Practice does make a difference, and going though the tutorial more than once made it so that I was able to get a good feel for the navigation of simple tasks that later on will be almost natural to us.

Lastly, the tutorial was boring since we had to repeat it a couple of times. The first time was fun though since it was interesting to see how we can set tables that will reflect real geospatial data and having two different data sets work with each other.  It really emphasizes the information that we received in lecture about specific ID's.  Here we get to see how they work by our manipulation.